Data selection for support vector machine classifiers

The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classifier, is formulated as a concave minimization problem and solved by a finite number of linear programs. This minimal set of data points, which is the smallest number of support vectors that completely characterize a separating plane classifier, is considerably smaller than that required by a standard 1-norm support vector machine with or without feature selection. The proposed approach also incorporates a feature selection procedure that results in a minimal number of input features used by the classifier. Tenfold cross validation gives as good or better test results using the proposed minimal support vector machine (MSVM) classifier based on the smaller set of data points compared to a standard 1-norm support vector machine classifier. The reduction in data points used by an MSVM classifier over those used by a 1-norm SVM classifier averaged 66% on...

• Type: Conference
• Year: 2000
• Where: KDD
• Authors: Glenn Fung, Olvi L. Mangasarian
• Keywords: 1-norm Support Vector, Data Mining, Data Points, KDD 2000, Support Vector Machines